Gensyn: A Decentralized AI Computing Network Democratizing AI through Decentralization
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Gensyn: A Decentralized AI Computing Network Democratizing AI through Decentralization
Gensyn's core goal is to democratize AI through decentralized initiatives, enabling more people to participate in the innovation and application of AI technology.
Navigating Web3 tides with focused insightsBy Dong Nuo
On June 12, Gensyn, a UK-based blockchain AI computing protocol, announced the completion of a $43 million Series A funding round led by a16z. This marks a milestone in the AI revolution, with Gensyn delivering one of the first tangible solutions.
At its core, Gensyn is a Layer 1 proof-of-stake blockchain based on the Substripe protocol that facilitates machine learning task allocation and reward distribution via smart contracts, accelerating AI model training while significantly reducing the cost of deep learning.
The cost of a single GPT-3 training run in 2020 was approximately $12 million—over 270 times higher than the estimated $43,000 cost for GPT-2 in 2019.
Typically, the complexity (size) of top neural network models doubles every three months. In contrast, Gensyn’s machine learning training costs about $0.40 per hour, far below AWS ($2) and GCP ($2.50).
Gensyn aims to leverage blockchain technology to create a decentralized, large-scale, distributed deep learning computation protocol featuring probabilistic proof-of-learning and cryptocurrency-based incentives.
Gensyn connects developers (anyone capable of training machine learning models) with solvers (individuals or entities willing to use their own hardware for model training).
By tapping into idle, machine learning-capable computing devices worldwide—including small data centers, personal gaming PCs, M1 and M2 Macs, and even smartphones—Gensyn links them into a global machine learning supercluster, increasing available computational power by 10 to 100 times.
Additionally, Gensyn achieves massive scale and low-cost, trustless neural network training through its innovative verification system and computing power supply mechanism.
1. Innovative Verification System
The core challenge for Gensyn is verifying whether computational tasks executed on devices have been performed correctly, and triggering token-based payments accordingly.
Gensyn addresses this verification problem through three key concepts: probabilistic proof-of-learning, graph-based precise localization protocols, and Truebit-style incentive games.
The system consists of four main participants: submitters, solvers, validators, and whistleblowers.
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Submitters are end users who provide computational tasks and pay for completed work units.
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Solvers perform the primary workload by executing model training and generating proofs for validators to verify.
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Validators link non-deterministic training processes to deterministic linear computations, replicate parts of the solver's proof, and compare results against expected thresholds.
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Whistleblowers act as the final line of defense, auditing validator work and challenging inaccuracies to claim accumulated rewards.
The system operates trustlessly, with overhead scaling linearly with model size, keeping verification costs constant. Its innovation lies in combining model training checkpoints with probabilistic on-chain verification, effectively solving state dependency issues in arbitrarily large neural network training.
2. New Computing Power Supply
Gensyn leverages underutilized and unoptimized computing resources, including idle gaming GPUs and former Ethereum PoW mining rigs.
As a decentralized protocol, it cannot be arbitrarily "shut down" without community consent, making it censorship-resistant—a key distinction from Web2 systems. The protocol innovates by fully utilizing idle computing devices, providing greater computational capacity to the community while offering new income opportunities for device owners.
Moreover, Gensyn offers GPU computing at prices comparable to data center offerings, with potential scale surpassing AWS.
In summary, Gensyn’s core goal is democratizing AI through decentralization, enabling broader participation in AI innovation and applications. The project’s central idea is to harness underutilized computing resources and build an open, decentralized verification system to improve the efficiency and accuracy of AI models, creating more opportunities for AI entrepreneurs.
It is an innovative and forward-looking initiative poised to play a significant role in the future of AI.
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